Hi all, I am working on a small data analysis project using Python to visualize pricing trends. I want to pull the burger and meal prices from my live site https://mcdonaldsmenu-prices.co.uk/ and turn them into a Matplotlib dashboard.
Right now, the data has different categories (individual items vs meal deals). What is the best practice for structuring a Pandas DataFrame when handling multi-tier pricing like this so it scales nicely when I add more locations? Any tips on optimizing the dictionary structure before converting to a DataFrame would be awesome.
Note: Website link provided for reference only! Thnaks.
I know there are good reasons for sticking with pandas sometimes. But polars has struct-type columns which can be a real help. Also because it’s very performant. Getting rid of those 0.1 second delays feels really nice.
Beyond that I can’t advice any particular structure for this data. I think I’d advice not overthinking it, and just mentally preparing yourself to restructure the data later as needed.
Peter thanks for the suggestion! I’ve been hearing a lot of good things about Polars lately, and those struct-type columns actually sound perfect for handling the nested meal variations. I might just pull a small sample into it tonight to see how it handles the data, getting rid of that slight Pandas lag definitely sounds like a nice bonus!!!
